In this K99/R00 application the candidate proposes to develop novel clinical informatics techniques to achieve detailed, objective, and quantitative measurements of brain anatomy and lesions. Our overall approach will be based on principles of computational differential geometry to allow us to handle arbitrary complex brain structures, such as the cortical surface and, more importantly, to map three-dimensional (3D) objects to a two- dimensional (2D) plane without loss to enable computationally tractable analyses. The brain remains one of the organs that is not completely understood to clinicians and researchers and, given this, brain imaging and brain MRI, in particular, now offset this by acquiring detailed images in high resolution. Despite these advances, our ability to analyze the brain itself lags behind due to the high complexity of brain structures that often cannot be adequately processed by existing computational techniques. For example, in clinics, it is common to track how brain tumors evolve and respond to treatment; however, as brain tumors manifest in very different shapes, automated analysis still is not available, and a clinician must resort to manual analysis to reach any conclusions. As another example, the brain cortex is responsible for many critical neurological functions but its large number of sulcus and folds makes it very challenging for a human observer to identify pathophysiological abnormalities. To address such challenges, the candidate plans to take advantage of the versatility and conformality offered by computational differential geometry to develop new informatics approaches to model and analyze brain images. One key principle of computational differential geometry is to construct a conformal mapping between a 3D structure and a 2D unit sphere, on which detailed analysis can be achieved and through which the results can be inversely mapped back to 3D. As a differential geometry setup, Ricci flow-based methods offer the benefits of guaranteed convergence and the ability to handle shapes with a large number of surfaces and holes and arbitrary curvature. Utilizing her expertise in computational differential geometry, the candidate will further develop Ricci flow-based techniques to model and analyze brain anatomy objectively and accurately to address clinical and research questions. The candidate has a solid background in applied mathematics and computer science. She has a multi- disciplinary mentoring team that will provide technical, clinical, and career development support to help her become an independent faculty member. The research and career development resources of the Brigham and Women's Hospital constitute a highly supportive environment. The findings of the project will advance clinical practice and scientific research by providing fast, objective, and quantitative characterization of the most complex brain structures for improved decision-making around diagnoses and prognoses. Therefore, the project will benefit human health by promoting our understanding of the brain in research and facilitating the clinical evaluations of brain anatomy.